object-detection-with-YOLO.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Detection with YOLO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Description: Detect and classify multiple objects within an image using the YOLO (You Only Look Once) algorithm.\n",
    "* Skills: Computer vision, deep learning, TensorFlow/PyTorch, object detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Set up the YOLO model.\n",
    "* Preprocess the dataset.\n",
    "* Train and evaluate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOLO object detection typically requires specialized frameworks like Darknet or using pre-trained models.\n",
    "# Below is an outline using a pre-trained YOLO model for inference.\n",
    "\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.preprocessing.image import img_to_array, load_img\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "# Load pre-trained YOLO model\n",
    "net = cv2.dnn.readNet(\"yolov3.weights\", \"yolov3.cfg\")\n",
    "layer_names = net.getLayerNames()\n",
    "output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n",
    "\n",
    "# Load and preprocess image\n",
    "image = cv2.imread(\"input.jpg\")\n",
    "height, width = image.shape[:2]  # Define height and width based on the loaded image's dimensions\n",
    "\n",
    "blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
    "net.setInput(blob)\n",
    "outs = net.forward(output_layers)\n",
    "\n",
    "# Display results\n",
    "for out in outs:\n",
    "    for detection in out:\n",
    "        scores = detection[5:]\n",
    "        class_id = np.argmax(scores)\n",
    "        confidence = scores[class_id]\n",
    "        if confidence > 0.5:\n",
    "            center_x = int(detection[0] * width)\n",
    "            center_y = int(detection[1] * height)\n",
    "            w = int(detection[2] * width)\n",
    "            h = int(detection[3] * height)\n",
    "            x = int(center_x - w / 2)\n",
    "            y = int(center_y - h / 2)\n",
    "            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
    "cv2.imshow(\"Image\", image)\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}